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Application of bivariate mixed counting process models to genetic analysis of rheumatoid arthritis severity.

Sutradhar R, Pinnaduwage D, Bull SB - BMC Proc (2007)

Bottom Line: We sought to i) identify putative genetic determinants of the severity of rheumatoid arthritis in the NARAC (North American Rheumatoid Arthritis Consortium) data, ii) assess whether known candidate genes for disease status are also associated with disease severity in those affected, and iii) determine whether heterogeneity among the severity phenotypes can be explained by genetic and/or host factors.These questions are addressed by developing bivariate mixed-counting process models for numbers of tender and swollen joints to evaluate genetic association of candidate polymorphisms, such as DRB1, and selected single-nucleotide polymorphisms in known candidate genes/regions for rheumatoid arthritis, including PTPN22, and those in the regions identified by a genome-wide linkage scan of disease severity using the dense Illumina single-nucleotide polymorphism panel.Moreover, we found a gain in efficiency when using a bivariate compared to a univariate counting process model.

View Article: PubMed Central - HTML - PubMed

Affiliation: Samuel Lunenfeld Research Institute of Mount Sinai Hospital, 60 Murray Street, Box #18, Lebovic Building, 5th Floor, Prosserman Centre, Toronto, Ontario M5T 3L9, Canada. rinku.sutradhar@ices.on.ca

ABSTRACT
We sought to i) identify putative genetic determinants of the severity of rheumatoid arthritis in the NARAC (North American Rheumatoid Arthritis Consortium) data, ii) assess whether known candidate genes for disease status are also associated with disease severity in those affected, and iii) determine whether heterogeneity among the severity phenotypes can be explained by genetic and/or host factors. These questions are addressed by developing bivariate mixed-counting process models for numbers of tender and swollen joints to evaluate genetic association of candidate polymorphisms, such as DRB1, and selected single-nucleotide polymorphisms in known candidate genes/regions for rheumatoid arthritis, including PTPN22, and those in the regions identified by a genome-wide linkage scan of disease severity using the dense Illumina single-nucleotide polymorphism panel. The counting process framework provides a flexible approach to account for the duration of rheumatoid arthritis, an attractive feature when modeling severity of a disease. Moreover, we found a gain in efficiency when using a bivariate compared to a univariate counting process model.

No MeSH data available.


Related in: MedlinePlus

Estimated mean number of counts versus time since RA onset.
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Figure 2: Estimated mean number of counts versus time since RA onset.

Mentions: We applied our bivariate mixed-counting process model for the severity phenotypes under various covariate models, yielding estimates and standard errors of β1, β2, and log φ (Table 1). The asymptotic confidence interval for log φ indicated a significant amount of extra-Poisson variation in the counts. That is, the number of tender joints varied considerably between patients, as did the number of swollen joints. The heterogeneity appeared to decrease as more covariates were added to the model, and any remaining variation in the model was captured under this random effects formulation. The log-likelihood increased dramatically as the covariates sex, smoking history, and DRB1 were added to the model. The likelihood ratio (LR) test for the joint contribution of sex to the bivariate model (2 degrees of freedom) provided a p-value less than 1 × 10-6, and the LR test for the joint contribution of DRB1 provided a p-value less than 0.0001. The Wald test detected a significant sex effect for the tender, but not for the swollen joint process. These sex differences were evident in plots of the estimated mean number of counts (Fig. 2). The LR test for the equality of DRB1 between the two counting processes in the bivariate model (1 degree of freedom) yielded a p-value less than 1 × 10-5.


Application of bivariate mixed counting process models to genetic analysis of rheumatoid arthritis severity.

Sutradhar R, Pinnaduwage D, Bull SB - BMC Proc (2007)

Estimated mean number of counts versus time since RA onset.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2367476&req=5

Figure 2: Estimated mean number of counts versus time since RA onset.
Mentions: We applied our bivariate mixed-counting process model for the severity phenotypes under various covariate models, yielding estimates and standard errors of β1, β2, and log φ (Table 1). The asymptotic confidence interval for log φ indicated a significant amount of extra-Poisson variation in the counts. That is, the number of tender joints varied considerably between patients, as did the number of swollen joints. The heterogeneity appeared to decrease as more covariates were added to the model, and any remaining variation in the model was captured under this random effects formulation. The log-likelihood increased dramatically as the covariates sex, smoking history, and DRB1 were added to the model. The likelihood ratio (LR) test for the joint contribution of sex to the bivariate model (2 degrees of freedom) provided a p-value less than 1 × 10-6, and the LR test for the joint contribution of DRB1 provided a p-value less than 0.0001. The Wald test detected a significant sex effect for the tender, but not for the swollen joint process. These sex differences were evident in plots of the estimated mean number of counts (Fig. 2). The LR test for the equality of DRB1 between the two counting processes in the bivariate model (1 degree of freedom) yielded a p-value less than 1 × 10-5.

Bottom Line: We sought to i) identify putative genetic determinants of the severity of rheumatoid arthritis in the NARAC (North American Rheumatoid Arthritis Consortium) data, ii) assess whether known candidate genes for disease status are also associated with disease severity in those affected, and iii) determine whether heterogeneity among the severity phenotypes can be explained by genetic and/or host factors.These questions are addressed by developing bivariate mixed-counting process models for numbers of tender and swollen joints to evaluate genetic association of candidate polymorphisms, such as DRB1, and selected single-nucleotide polymorphisms in known candidate genes/regions for rheumatoid arthritis, including PTPN22, and those in the regions identified by a genome-wide linkage scan of disease severity using the dense Illumina single-nucleotide polymorphism panel.Moreover, we found a gain in efficiency when using a bivariate compared to a univariate counting process model.

View Article: PubMed Central - HTML - PubMed

Affiliation: Samuel Lunenfeld Research Institute of Mount Sinai Hospital, 60 Murray Street, Box #18, Lebovic Building, 5th Floor, Prosserman Centre, Toronto, Ontario M5T 3L9, Canada. rinku.sutradhar@ices.on.ca

ABSTRACT
We sought to i) identify putative genetic determinants of the severity of rheumatoid arthritis in the NARAC (North American Rheumatoid Arthritis Consortium) data, ii) assess whether known candidate genes for disease status are also associated with disease severity in those affected, and iii) determine whether heterogeneity among the severity phenotypes can be explained by genetic and/or host factors. These questions are addressed by developing bivariate mixed-counting process models for numbers of tender and swollen joints to evaluate genetic association of candidate polymorphisms, such as DRB1, and selected single-nucleotide polymorphisms in known candidate genes/regions for rheumatoid arthritis, including PTPN22, and those in the regions identified by a genome-wide linkage scan of disease severity using the dense Illumina single-nucleotide polymorphism panel. The counting process framework provides a flexible approach to account for the duration of rheumatoid arthritis, an attractive feature when modeling severity of a disease. Moreover, we found a gain in efficiency when using a bivariate compared to a univariate counting process model.

No MeSH data available.


Related in: MedlinePlus